Prediction of weight and aerobic fitness with resting-state fMRI

Obesity is a worldwide problem, caused, in part, by
self-regulatory failure of eating behavior. Understanding factors that predict long-term
weight fluctuation will be important to identify possible preventative steps. Emerging
evidence suggests that neural reward-responsivity is associated with weight
gain and eating behaviors. Cue-reactivity
of the nucleus accumbens in response to food images has been shown to
predict subsequent weight gain in female college freshmen. Dieters who
have recently experienced a self-regulatory failure also show increased cue-reactivity
in response to food images compared to those who have not broken their diets (Demos
et al., 2010). Alternatively, high levels of physical activity contribute to
long-term successful weight loss maintenance (Wing & Hill, 2001). Aerobic exercise has been shown to
upregulate a variety of trophic factors, improve cognition and reduce depression.
Identifying the resting neural activity associated with high levels of physical
fitness may play an important role in understanding how body mass and aerobic
capacity influence cognition. This study uses resting-state functional
connectivity to identify neural predictors of Body Mass Index and aerobic
capacity (VO2max).

Methods

Subjects completed a survey including questions related to
height, weight, frequency of physical activity and perceived physical ability
(George et al., 1997) that were used to estimate aerobic capacity. Subjects
viewed a white fixation cross on a black background for 2 runs of 5 minutes each
(BOLD fMRI). Subjects were instructed to simply stay awake, refrain from moving
and look at the crosshair. Scan parameters included voxel size of 3x3x3.5,
TR=2.5 S and TE=35ms. Standard preprocessing techniques were used and
time-series were extracted from 160 regions attributed to 6 functional networks
(Dosenbach et al, 2010). Connectivity between each pair of regions was used to
predict BMI and VO2max using Support Vector Regression.

Results and Discussion

Functional connectivity, both within and between regions from
each of the 6 resting-state networks used in the current study, was found to be
important in predicting aerobic capacity and BMI (p<0.001). These results
suggest that resting-state neural activity related to BMI and aerobic capacity
are diverse and can in part be predicted by within-network coherence and
between network interactions.